Published Paper Details:

Video summarization is a fundamental task of computer vision and multimedia processing to condense lengthy videos into short representations without losing the valuable content and context. The study is founded on the use of object detection techniques in video summarization and relies on the capability of deep learning to automatically recognize and extract discriminative objects and events from video streams. Relying on the benefit of the latest object detection models and new summarization techniques, the study tries to enhance the efficiency and effectiveness of video summarization to allow users to quickly perceive the content and meaning of videos without the requirement of lengthy playback. The approach not only enhances video browsing and comprehension of content but also has its future areas of application in surveillance, video indexing, and content recommendation systems. Video summarization is a crucial element to successfully extract key moments from lengthy video recordings, reducing storage and processing costs, and enhancing the user experience. The work proposes a state-of-the-art video summarization technique founded on real-time object detection based on YOLOv3 and Deep SORT algorithms. Based on the fusion of the new approaches, the proposed method effectively extracts and tracks discriminative objects with improved efficiency, leading to informative and meaningful video summaries. Experimental results exhibit enhanced efficiency and accuracy compared to state-of-the-art summarization techniques, indicating the potentiality of the proposed methodology in its real-world applications like surveillance, sport analysis, and content generation.